This repo outlines the steps required for a complete AI/ML cycle. In involves Cumulocity IoT with the addintional components DataHub for offloading the process data:
- Export data through DataHub to AWS S3
- Locate Data in AWS S3
- Train Model in AWS SageMaker and export in ONNX format
- Deploy Scoring Microservice in Cumulocity
- Deploy Ananlytics EPL App for scoring
- Scoring results
They can they can be viewed in the following diragram:
The out-of-the box extension points, e.g. Cumulocity Microservices & DataHub allow to adapt this solution to your AI/ML requirements.
The following commands were used to import the simulation data to Cumulocity:
c8y util repeatcsv --first 1 activity-recognition-demo/data/c8y_Acceleration_Merged_Shuffle_mod.csv | \
c8y measurements create --device 5558565188 --template "{'time': _.Date(input.value.time, '3months' ), 'c8y_Acceleration': {'accelerationX': {'value': input.value.accelerationX , 'label': input.value.label}, 'accelerationY': {'value': input.value.accelerationY , 'label': input.value.label} , 'accelerationZ': {'value': input.value.accelerationZ , 'label': input.value.label} } , type: 'c8y_Acceleration'}" --dry
In this command you have to update the time offset to match the current date _.Date(input.value.time, '3months').
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- Data integration options with Cumulocity IoT
- Cumulocity IoT Web Development Tutorial - Part 1: Start your journey
- The power of micro frontends – How to dynamically extend Cumulocity IoT Frontends
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